Modern large-scale information retrieval techniques often use index-based techniques. Indexing and indexing-based techniques can allow offline processing that can greatly speed information retrieval. This can be of great value in applications such as online advertising or search, where results or advertisements may need to be determined in just a small fraction of a second. In some techniques, particular elements or items of a match, such as content of a Web page or content of advertising, are indexed. Matrices can be formed using indexed information. Indexing techniques can then be applied, such as techniques that utilize inner product matrix multiplication to compute or help compute, for example, associations, matches, strengths of associations or matches, or corresponding scores. Such techniques may be used, for example, in determining a top set of advertisements that match a particular serving opportunity, such as advertisements, including creatives, that best match a keyword query, Web site content, using or other targeting information, etc.
Performance parameters associated with advertising and other content, such as click through rate, are critical for many uses and applications. Furthermore, predicted click through rates for particular situations that arise in real time can be critical for optimal matching. For example, in advertising applications, predicted performance of an advertisement in a given context, such as predicted click through rate, is often used in determining a good or optimal set of advertisements to serve in relation to a given serving opportunity. For instance, in sponsored search, the rank of an advertisement may be determined by an associated bid amount multiplied by an associated determined predicted click through rate (perhaps in combination with other parameters or factors, etc).
Machine learning techniques can be of great value in information retrieval, including determining top matches. For example, in advertising, machine learning techniques are often used in determining a top set of matches between advertisements and a serving opportunity. For example, machine learning can be used to determine strength of matching between new sets of items, based on features, such as content-based features, relating to such items. Training sets of collected historical information can be used to train a machine learning model, which model can then be applied to new situations and sets or combinations of items. For example, machine learning techniques can be used to determine predicted click through rates associated with matches. However, use of existing machine learning models on new situations can be computationally intensive and time-consuming.
There is a need for faster and more efficient methods and systems for determining predicted click through rate and top sets of matched items such as advertisements.